similar to: variable scope for deltavar function from emdbook

I have data that looks like O.lengthO.age 176 1 179 1 182 1 ... 493 5 494 5 514 5 606 5 462 6 491 6 537 6 553 6 432 7 522 7 625 8 661 8 687 10 704 10 615 12 (truncated) with a simple VonB growth model from within nls(): plot(O.length~O.age, data=OS) Oto = nls(O.length~Linf*(1-exp(-k*(O.age-t0))), data=OS, start=list(Linf=1000, k=0.1, t0=0.1), trace=TRUE) mod <- seq(0, 12)

I am using curve3d in the emdbook package to graph a gaussian copula density function generated via the copula package. Unfortunately, it appears that emdbook masks dmvnorm from the package mvtnorm in a way that prohibits copula from generating the gaussian copula. (Sounds very confusing!) For example, > library(copula) > f<-function(x,y) dcopula(normalCopula(0),c(x,y)) >

Hello, I am using rbetabinom ( to generate beta binomial random variables) function available in the "emdbook"package written by Professor. Ben Bolker for my research study. I have no questions with this function. However, I am looking for the theoretical method/algorithm of the function "rbetabinom " . Morris (1997), American Naturalist 150:299-327 is given as the reference

this is probably not really a R specific question, if so apologies for off-topic posting: I'm interested in the probability density function of the maximum values from repeated samples of size N from a normal distribution: smp <- rnorm(N, meanval, stdev) with some mean 'meanval' and standard deviation 'stdev'. I would like to know what is the frequency distribution of

Hello, I am facing a problem with optimization in R from 2-3 weeks. I have some Gaussian mixtures parameters and I want to find the maximum in that *Parameters are in the form * mean1 mean2 mean3 sigma1 sigma2 sigma3 c1 c2 c3 506.8644 672.8448 829.902 61.02859 9.149168 74.84682 0.1241933 0.6329082 0.2428986 I have used optima and optimx to find the

Dear all, Is there a way to retrieve standard errors from nls models? The help page tells me that arguments such as se.fit are ignored... Many thanks and best wishes Christoph -- Dr. rer.nat. Christoph Scherber University of Goettingen DNPW, Agroecology Waldweg 26 D-37073 Goettingen Germany phone +49 (0)551 39 8807 fax +49 (0)551 39 8806 Homepage http://www.gwdg.de/~cscherb1

Dear R-helpers, I´m in the context of writing a general function for error propagation in R. There are somehow a few questions I would like to ask (discuss), as my statistical knowledge is somewhat restricted. Below is the function I wrote, the questions are marked. Many thanks in advance. propagate <- function(expr, varList, type = c("stat", "raw"), cov = TRUE) {

Hi I want to do simulation on quasi-binomial distribution with some covariates. Does anyone have an idea how to do that? [[alternative HTML version deleted]]

Hi all, I have some problems with the mle2 function > RogersIIbinom <- function(N0,attackR3_B,Th3_B) {N0-lambertW(attackR3_B*Th3_B*N0*exp(-attackR3_B*(24-Th3_B*N0)))/(attackR3_B*Th3_B)} > RogersII_B <- mle2(FR~dbinom(size=N0,prob=RogersIIbinom(N0,attackR3_B,Th3_B)/N0),start=list(attackR3_B=1.5,Th3_B=0.04),method="Nelder-Mead",data=dat) Error in dbinom(x, size, prob, log)

Hello, I have a log likelihood function that I was able to optimize using mle2. I have two years of the data used to fit the function and I would like to fit both years simultaneously to test if the model parameter estimates differ between years, using likelihood ratio tests and AIC. Can anyone give advice on how to do this? My likelihood functions are long so I'll use the tadpole

Hello, I have got a question. I've got a matrix (mail end) with the colnames x, y, z. In this matrix are different measurements. x and y are risign coordinates. My question. Always, if the "x" AND "y" coordinates are the same, I want to get the mean of their z values. e.q. " x" AND "y" in line1 and line8 are identical: 29 4.5 --> mean of

If I want to find out the soltion of X1,X2 that min(3X1+2X2+X1X2) subject to 20<=X1+3X2<=50 10<=X1 which function or package can I use? Thanks.

Hello, All: I'm getting strange errors with write.csv with some objects of class c('findFn', 'data.frame'). Consider the following: df1 <- data.frame(x=1) class(df1) <- c('findFn', 'data.frame') write.csv(df1, 'df1.csv') # Error in x$Package : $ operator is invalid for atomic vectors df2 <- data.frame(a=letters[1:2],

Hi, Peter et al.: On 2017-01-09 4:24 AM, peter dalgaard wrote: > On 09 Jan 2017, at 10:53 , Spencer Graves <spencer.graves at prodsyse.com> wrote: > >> # Define an object of class 'dum' >> k <- 1 >> class(k) <- 'dum' >> str(k) # as expected >> >> # Define print.dum >> print.dum <- function(x, ...) >>

?s 17:02 de 28/06/2024, Spencer Graves escreveu: > Hello, All: > > > ????? I'm getting strange errors with write.csv with some objects of > class c('findFn', 'data.frame'). Consider the following: > > > df1 <- data.frame(x=1) > class(df1) <- c('findFn', 'data.frame') > write.csv(df1, 'df1.csv') > # Error in

Hi, All: I'm having trouble getting deparse(substitute(x)) inside print.generic to consistently I'm having trouble getting a print.something to work consistently. Consider the following toy example: # Define an object of class 'dum' k <- 1 class(k) <- 'dum' str(k) # as expected # Define print.dum print.dum <- function(x, ...)

I haven't been an R lister for a bit, but I hope to enlist someone's help here. I think this is a simple question, so I hope the answer is not much trouble. Can you please respond directly to this email address in addition to the list (if responding to the list is warranted)? I have a histogram and I want to see if the data fit a Poisson distribution. How do I do this? It is

Hello everyone, I have this problem, when I launch findFn from RStudio ( but not from R console) the table of results does not pop in the browser and I got a warning. findFn("test") found 9914 matches; retrieving 20 pages, 400 matches. 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Warning message: In parseHTML(href) : NAs introduced by coercion Thanks H. [[alternative HTML

I can take the results of a simulation with one random variable and generate an empirical interval that contains 95% of the observations, e.g., x <- rnorm(10000) quantile(x,probs=c(0.025,0.975)) Is there an R function that can take the results from two random variables and generate an empirical ellipse that contains 95% of the observations, e.g., x <- rnorm(10000) y <- rnorm(10000) ?

I have a time series with interval of 2.5 minutes, or 24 observations per hour. I am trying to find a 1 hr moving average, looking backward, so that moving average at n = mean(n-23 : n) The time series has about 1.5 million rows, with occasional gaps due to poor data quality. I only want to take a 1 hour moving average for those periods that are complete, i.e. have 24 observations in the